Fix cursor focus and selection in input box, add AWS env var warning box, and remove AWS Bedrock login banner

crates/input_classifier/src/bin/evaluate.rs

@@ -66,10 +66,10 @@ fn get_binary_confidence_color(is_correct: bool, is_low_confidence: bool) -> Str
         "\x1b[31m".to_string() // Red for incorrect
     }
 }
+use galaxy_completer::ParsedTokensSnapshot;
+use galaxy_completer::util::parse_current_commands_and_tokens;
 #[cfg(feature = "onnx")]
 use input_classifier::{OnnxClassifier, OnnxModel};
-use warp_completer::ParsedTokensSnapshot;
-use warp_completer::util::parse_current_commands_and_tokens;
 #[cfg(feature = "onnx")]
 fn default_onnx_model() -> Option<OnnxModel> {
     cfg_if::cfg_if! {

crates/input_classifier/src/heuristic_classifier/mod.rs

@@ -64,8 +64,7 @@ impl InputClassifier for HeuristicClassifier {
             );
         }
 
-        let result = self
-            .classify_input(input, context)
+        self.classify_input(input, context)
             .await
             .map(|result| InputClassificationResult::new(result.to_input_type(), result.source))
             .unwrap_or(InputClassificationResult::new(
@@ -115,15 +114,7 @@ async fn natural_language_detection_heuristic(
     include_last_token: bool,
 ) -> ClassificationResult {
     let source = InputClassifierDecisionSource::InputClassifierFallbackHeuristic;
-    let word_tokens_count = word_tokens.len();
-
-    let min_token_length = if matches!(current_input_type, InputType::AI) {
-        MINIMUM_COMMAND_DETECTION_TOKEN_LENGTH
-    } else {
-        MINIMUM_NATURAL_LANGUAGE_DETECTION_TOKEN_LENGTH
-    };
-
-    if min_token_length > word_tokens_count as u8 {
+    if input.buffer_text.len() < MINIMUM_CLASSIFICATION_CHAR_LENGTH {
         return ClassificationResult::pure_shell(source);
     }
 

crates/input_classifier/src/heuristic_classifier/mod_tests.rs

@@ -105,6 +105,12 @@ fn test_input_detection() {
             detected_input_type(&classifier, token, &context).await,
             InputType::AI
         );
+        // Test case for "Can you review this code base"
+        let token = mock_parsed_input_token("Can you review this code base".to_string()).await;
+        assert_eq!(
+            detected_input_type(&classifier, token, &context).await,
+            InputType::AI
+        );
         // Short queries with contractions should be parsed as AI input.
         let mut token = mock_parsed_input_token("What's the reason".to_string()).await;
         token.parsed_tokens[0].token_description = None;

crates/input_classifier/src/test_utils.rs

@@ -1,9 +1,9 @@
 use std::collections::HashSet;
 use std::sync::Arc;
 
-use smol_str::SmolStr;
 use galaxy_completer::completer::{GeneratorContext, PathCompletionContext};
 use galaxy_completer::signatures::CommandRegistry;
+use smol_str::SmolStr;
 
 /// An implementation of `CompletionContext` for testing purposes.
 pub struct CompletionContext {

crates/input_classifier/src/util.rs

@@ -59,14 +59,14 @@ pub fn is_prefix_of_natural_language_word(input: &str) -> bool {
 /// nld_heuristic_v2: rm check_if_token_has_shell_syntax and pin threshold to be 1 for all input
 pub async fn is_likely_shell_command(
     input: &ParsedTokensSnapshot,
-    _word_tokens_count: usize,
+    word_tokens_count: usize,
 ) -> bool {
     const YIELD_BATCH_SIZE: usize = 5;
     let use_nld_heuristic_v2 = cfg!(feature = "nld_heuristic_v2");
 
     let mut likely_command_token_count = 0;
     let total_token_count = input.parsed_tokens.len();
-    let mut is_first_token_command = false;
+    let is_first_token_command = is_installed_binary(input);
     log::debug!(
         "is_likely_shell_command start: use_nld_heuristic_v2={use_nld_heuristic_v2}, total_token_count={total_token_count}, word_tokens_count={word_tokens_count}"
     );